Search

In power systems, optimal power flow (OPF) is a complex and constrained optimization problem in which quite often multiple and conflicting objectives are required to be optimized. The traditional way of dealing with multi-objective OPF (MOOPF) is the weighted sum method which converts the multi-objective OPF into a single-objective problem and provides a single solution from the set of Pareto solutions. This paper presents MOOPF study applying multi-objective evolutionary algorithm based on decomposition (MOEA/D) where a set of non-dominated solutions (Pareto solutions) can be obtained in a single run of the algorithm. OPF is formulated with two or more objectives among fuel (generation) cost, emission, power loss and voltage deviation. The other important aspect in OPF problem is about satisfying power system constraints. As the search process adopted by evolutionary algorithms is unconstrained, for a constrained optimization problem like OPF, static penalty function approach has been extensively employed to discard infeasible solutions. This approach requires selection of a suitable penalty coefficient, largely done by trial-and-error, and an improper selection may often lead to violation of system constraints. In this paper, an effective constraint handling method, superiority of feasible solutions (SF), is used in conjunction with MOEA/D to handle network constraints in MOOPF study. The algorithm MOEA/D-SF is applied to standard IEEE 30-bus and IEEE 57-bus test systems. Simulation results are analyzed, especially for constraint violation and compared with recently reported results on OPF. [ABSTRACT FROM AUTHOR]

Different constraint handling techniques have been used with multi-objective evolutionary algorithms (MOEA) to solve constrained multi-objective optimization problems. It is impossible for a single constraint handling technique to outperform all other constraint handling techniques always on every problem irrespective of the exhaustiveness of the parameter tuning. To overcome this selection problem, an ensemble of constraint handling methods (ECHM) is used to tackle constrained multi-objective optimization problems. The ECHM is integrated with a multi-objective differential evolution (MODE) algorithm. The performance is compared between the ECHM and the same single constraint handling methods using the same MODE (using codes available from http://www3.ntu.edu.sg/home/EPNSugan/index.htm). The results show that ECHM overall outperforms the single constraint handling methods. [ABSTRACT FROM AUTHOR]

The global best (gbest) or local best (lbest) of every particle in state-of-the-art multi-objective particle swarm optimization (MOPSO) implementations is selected from the non-dominated solutions in the external archive. This approach emphasizes the elitism at the expense of diversity when the size of the current set of non-dominated solutions in the external archive is small. This article proposes that the gbests or lbests should be chosen from the top fronts in a non-domination sorted external archive of reasonably large size. In addition, a novel two local bests (lbest) based MOPSO (2LB-MOPSO) version is proposed to focus the search around small regions in the parameter space in the vicinity of the best existing fronts unlike the current MOPSO variants in which the pbest and gbest (or lbest) of a particle can be located far apart in the parameter space thereby potentially resulting in a chaotic search behaviour. Comparative evaluation using 19 multi-objectives test problems and 11 state-of-the-art multi-objective evolutionary algorithms ranks overall the 2LB-MOPSO as the best while two state-of-the-art MOPSO algorithms are ranked the worst with respect to other multi-objective evolutionary algorithms. [ABSTRACT FROM AUTHOR]

This article presents a local-best harmony search algorithm with dynamic subpopulations (DLHS) for solving the bound-constrained continuous optimization problems. Unlike existing harmony search algorithms, the DLHS algorithm divides the whole harmony memory (HM) into many small-sized sub-HMs and the evolution is performed in each sub-HM independently. To maintain the diversity of the population and to improve the accuracy of the final solution, information exchange among the sub-HMs is achieved by using a periodic regrouping schedule. Furthermore, a novel harmony improvisation scheme is employed to benefit from good information captured in the local best harmony vector. In addition, an adaptive strategy is developed to adjust the parameters to suit the particular problems or the particular phases of search process. Extensive computational simulations and comparisons are carried out by employing a set of 16 benchmark problems from the literature. The computational results show that, overall, the proposed DLHS algorithm is more effective or at least competitive in finding near-optimal solutions compared with state-of-the-art harmony search variants. [ABSTRACT FROM AUTHOR]

Introduces a genetic algorithm (GA)-based optimization procedure for structural pattern recognition in a model-based recognition system using attributed relational graph (ARG) matching technique. ARG representation; Application of GA for ARG matching; Summary of the pose-clustering algorithm; Presentation of experimental results and discussion.

The organization of video information in video databases requires automatic temporal segmentation with minimal user interaction. As neural networks are capable of learning the characteristics of various video segments and clustering them accordingly, in this paper, a neural network based technique is developed to segment the video sequence into shots automatically and with a minimum number of user-defined parameters. We propose to employ growing neural gas (GNG) networks and integrate multiple frame difference features to efficiently detect shot boundaries in the video. Experimental results are presented to illustrate the good performance of the proposed scheme on real video sequences. [ABSTRACT FROM AUTHOR]

Provides information on a study wherein a hierarchical overlapped self-organizing map (HOSOM) with limited structure adaptation capabilities was developed. Description of HOSOM; Experimental results; Conclusion.

In this study, we propose an efficient optimization algorithm that is a hybrid of the iterated greedy and simulated annealing algorithms (hereinafter, referred to as IGSA) to solve the flexible job shop scheduling problem with crane transportation processes (CFJSP). Two objectives are simultaneously considered, namely, the minimization of the maximum completion time and the energy consumptions during machine processing and crane transportation. Different from the methods in the literature, crane lift operations have been investigated for the first time to consider the processing time and energy consumptions involved during the crane lift process. The IGSA algorithm is then developed to solve the CFJSPs considered. In the proposed IGSA algorithm, first, each solution is represented by a 2-D vector, where one vector represents the scheduling sequence and the other vector shows the assignment of machines. Subsequently, an improved construction heuristic considering the problem features is proposed, which can decrease the number of replicated insertion positions for the destruction operations. Furthermore, to balance the exploration abilities and time complexity of the proposed algorithm, a problem-specific exploration heuristic is developed. Finally, a set of randomly generated instances based on realistic industrial processes is tested. Through comprehensive computational comparisons and statistical analyses, the highly effective performance of the proposed algorithm is favorably compared against several efficient algorithms. Note to Practitioners—The flexible job shop scheduling problem (FJSP) can be extended and applied to many types of practical manufacturing processes. Many realistic production processes should consider the transportation procedures, especially for the limited crane resources and energy consumptions during the transportation operations. This study models a realistic production process as an FJSP with crane transportation, wherein two objectives, namely, the makespan and energy consumptions, are to be simultaneously minimized. This study first considers the height of the processing machines, and therefore, the crane lift operations and lift energy consumptions are investigated. A hybrid iterated greedy algorithm is proposed for solving the problem considered, and several problem-specific heuristics are embedded to balance the exploration and exploitation abilities of the proposed algorithm. In addition, the proposed algorithm can be generalized to solve other types of scheduling problems with crane transportations. [ABSTRACT FROM AUTHOR]

Lipocalins are functionally diverse proteins that are composed of 120–180 amino acid residues. Members of this family have several important biological functions including ligand transport, cryptic coloration, sensory transduction, endonuclease activity, stress response activity in plants, odorant binding, prostaglandin biosynthesis, cellular homeostasis regulation, immunity, immunotherapy and so on. Identification of lipocalins from protein sequence is more challenging due to the poor sequence identity which often falls below the twilight zone. So far, no specific method has been reported to identify lipocalins from primary sequence. In this paper, we report a support vector machine (SVM) approach to predict lipocalins from protein sequence using sequence-derived properties. LipoPred was trained using a dataset consisting of 325 lipocalin proteins and 325 non-lipocalin proteins, and evaluated by an independent set of 140 lipocalin proteins and 21,447 non-lipocalin proteins. LipoPred achieved 88.61% accuracy with 89.26% sensitivity, 85.27% specificity and 0.74 Matthew’s correlation coefficient (MCC). When applied on the test dataset, LipoPred achieved 84.25% accuracy with 88.57% sensitivity, 84.22% specificity and MCC of 0.16. LipoPred achieved better performance rate when compared with PSI-BLAST, HMM and SVM-Prot methods. Out of 218 lipocalins, LipoPred correctly predicted 194 proteins including 39 lipocalins that are non-homologous to any protein in the SWISSPROT database. This result shows that LipoPred is potentially useful for predicting the lipocalin proteins that have no sequence homologs in the sequence databases. Further, successful prediction of nine hypothetical lipocalin proteins and five new members of lipocalin family prove that LipoPred can be efficiently used to identify and annotate the new lipocalin proteins from sequence databases. The LipoPred software and dataset are available at . [ABSTRACT FROM AUTHOR]

Background: Odorant binding proteins (OBPs) are believed to shuttle odorants from the environment to the underlying odorant receptors, for which they could potentially serve as odorant presenters. Although several sequence based search methods have been exploited for protein family prediction, less effort has been devoted to the prediction of OBPs from sequence data and this area is more challenging due to poor sequence identity between these proteins. Results: In this paper, we propose a new algorithm that uses Regularized Least Squares Classifier (RLSC) in conjunction with multiple physicochemical properties of amino acids to predict odorantbinding proteins. The algorithm was applied to the dataset derived from Pfam and GenDiS database and we obtained overall prediction accuracy of 97.7% (94.5% and 98.4% for positive and negative classes respectively). Conclusion: Our study suggests that RLSC is potentially useful for predicting the odorant binding proteins from sequence-derived properties irrespective of sequence similarity. Our method predicts 92.8% of 56 odorant binding proteins non-homologous to any protein in the swissprot database and 97.1% of the 414 independent dataset proteins, suggesting the usefulness of RLSC method for facilitating the prediction of odorant binding proteins from sequence information. [ABSTRACT FROM AUTHOR]

A method of using particle swarm optimisation (PSO) algorithms to optimise the element spacing and lengths of Yagi--Uda antennas is presented. SuperNEC, an object-oriented version of the numerical electromagnetic code (NEC-2) is used to evaluate the performance of various Yagi--Uda antenna designs. In order to show the capabilities of the PSO algorithm in Yagi--Uda antenna design, three different antenna design cases are optimised for various performance specifications. The three objectives considered are gain only, gain and input impedance only, and gain, input impedance and relative sidelobe level (rSLL). To alleviate the premature convergence problem of PSO, a novel learning strategy is employed. Each design problem is optimised using three variants of PSO algorithms, namely the modified PSO, fitness-distance ratio PSO (FDR-PSO), and comprehensive learning PSO (CLPSO). For the purpose of comparison and benchmarking, equally spaced arrays, genetic algorithm optimised antenna design, and computational intelligence optimised antenna design are considered. The results clearly show that the CLPSO is a robust and useful optimisation tool for designing Yagi antennas for the desired target specifications. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel mixed-integer optimization formulation for the optimal design of a reconfigurable antenna array with quantized phase excitations, which is solved by using the generalized generation gap (G3) model genetic algorithm (GA). To reduce effect of mutual coupling between the antenna-array elements, the amplitude excitations' dynamic-range ratio is minimized. The simulation experiments* clearly show an improved performance of the discrete realization of the phase-excitation values, as compared to the continuous realization and subsequent quantization. The effect of quantization on the continuous formulation of phase excitations is also demonstrated. The proposed mixed-integer formulation can be used in practice for the optimal design of dual-beam antenna arrays. © 2005 Wiley Periodicals, Inc. Microwave Opt Technol Lett 45: 461–465, 2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.20853 [ABSTRACT FROM AUTHOR]

Optimal power flow (OPF) is one of the challenging optimization problems in power domain. The complexity of the problem escalates with incorporation of uncertain and intermittent renewable sources into the electrical network. Flexible AC transmission system (FACTS) devices are also becoming more commonplace in modern power system to mitigate growing demand and to relieve congestion from the network. This paper aims to solve the OPF where the generation cost is optimized with incorporation of stochastic wind power and several types of FACTS devices such as static VAR compensator, thyristor-controlled series compensator and thyristor-controlled phase shifter. Case studies with both fixed and uncertain load demands are performed. The stochastic wind energy and load demand are modeled using suitable probability density functions. Optimization objective considers cost of thermal generation, direct cost of scheduled wind power, penalty cost for underestimation and reserve cost for overestimation of the wind power. In addition, both locations and ratings of the FACTS devices are optimized to minimize total generation cost of the system. Success history-based adaptive differential evolution (SHADE), a powerful evolutionary algorithm, is adopted to perform the optimization task. The constraints of OPF problem are handled using superiority of feasible solutions (SF) method. The integration approach of SF method with several popular metaheuristic algorithms has been proposed in this work, and a detailed comparative analysis among various algorithms establishes SHADE algorithm to be the best performer. [ABSTRACT FROM AUTHOR]

Clean energy resources such as wind power are playing an important role in power generation recently. In this paper, a modified version of multi-objective differential evolution (MODE) is used to tackle the extended dynamic economic emission dispatch (DEED) problem by incorporating wind power plant into the system. DEED is a nonlinear and highly constrained multi-objective optimization problem and the predicted load is varying with time. Fuel costs and pollution emission are the two objectives to be optimized and the valve point effect, spinning reserve, real power loss as well as the ramping rate are considered. To solve the model effectively, an ensemble of selection method is used in the MODE algorithm. The real-time output adjustment and penalty factor methods are used to deal with the complex constraints. The proposed method is firstly examined on several multi-objective benchmark problems and the DEED problem without considering the wind power to test its effectiveness of solving multi-objective optimization problems. Secondly, the model considering wind power is solved and the results show that the proposed algorithm is effective in handling such problems. [ABSTRACT FROM AUTHOR]

Portfolio optimization problems involve selection of different assets to invest in order to maximize the overall return and minimize the overall risk simultaneously. The complexity of the optimal asset allocation problem increases with an increase in the number of assets available to select from for investing. The optimization problem becomes computationally challenging when there are more than a few hundreds of assets to select from. To reduce the complexity of large-scale portfolio optimization, two asset preselection procedures that consider return and risk of individual asset and pairwise correlation to remove assets that may not potentially be selected into any portfolio are proposed in this paper. With these asset preselection methods, the number of assets considered to be included in a portfolio can be increased to thousands. To test the effectiveness of the proposed methods, a Normalized Multiobjective Evolutionary Algorithm based on Decomposition (NMOEA/D) algorithm and several other commonly used multiobjective evolutionary algorithms are applied and compared. Six experiments with different settings are carried out. The experimental results show that with the proposed methods the simulation time is reduced while return-risk trade-off performances are significantly improved. Meanwhile, the NMOEA/D is able to outperform other compared algorithms on all experiments according to the comparative analysis. [ABSTRACT FROM AUTHOR]

Real-world datasets commonly have issues with data imbalance. There are several approaches such as weighting, sub-sampling, and data modeling for handling these data. Learning in the presence of data imbalances presents a great challenge to machine learning. Techniques such as support-vector machines have excellent performance for balanced data, but may fail when applied to imbalanced datasets. In this paper, we propose a new undersampling technique for selecting instances from the majority class. The performance of this approach was evaluated in the context of several real biological imbalanced data. The ratios of negative to positive samples vary from ~9:1 to ~100:1. Useful classifiers have high sensitivity and specificity. Our results demonstrate that the proposed selection technique improves the sensitivity compared to weighted support-vector machine and available results in the literature for the same datasets. [ABSTRACT FROM AUTHOR]